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Hierarchical recurrent neural network for skeleton based action recognition (2015)
| Content Provider | CiteSeerX |
|---|---|
| Author | Wang, Wei Wang, Liang Du, Yong |
| Description | in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| Abstract | Human actions can be represented by the trajectories of skeleton joints. Traditional methods generally model the spatial structure and temporal dynamics of human skeleton with hand-crafted features and recognize human actions by well-designed classifiers. In this paper, considering that re-current neural network (RNN) can model the long-term con-textual information of temporal sequences well, we propose an end-to-end hierarchical RNN for skeleton based action recognition. Instead of taking the whole skeleton as the in-put, we divide the human skeleton into five parts accord-ing to human physical structure, and then separately feed them to five subnets. As the number of layers increases, the representations extracted by the subnets are hierarchically fused to be the inputs of higher layers. The final represen-tations of the skeleton sequences are fed into a single-layer perceptron, and the temporally accumulated output of the perceptron is the final decision. We compare with five other deep RNN architectures derived from our model to verify the effectiveness of the proposed network, and also com-pare with several other methods on three publicly available datasets. Experimental results demonstrate that our model achieves the state-of-the-art performance with high compu-tational efficiency. 1. |
| File Format | |
| Publisher Date | 2015-01-01 |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Conference Proceedings |
